Identifying metastatic ability of prostate cancer cell lines using native fluorescence spectroscopy and machine learning methods

作者：Jianpeng Xue, Yang Pu, Jason T. Smith, Xin Gao, Chun Wang, Binlin Wu · 发表于：Scientific Reports · 年份：2021 · DOI：10.1038/s41598-021-81945-7 · 被引用次数：19 · 研究领域：Spectroscopy Techniques in Biomedical and Chemical Research、Metabolomics and Mass Spectrometry Studies、Nanoplatforms for cancer theranostics

Metastasis is the leading cause of mortalities in cancer patients due to the spreading of cancer cells to various organs. Detecting cancer and identifying its metastatic potential at the early stage is important. This may be achieved based on the quantification of the key biomolecular components within tissues and cells using recent optical spectroscopic techniques. The aim of this study was to develop a noninvasive label-free optical biopsy technique to retrieve the characteristic molecular information for detecting different metastatic potentials of prostate cancer cells. Herein we report using native fluorescence (NFL) spectroscopy along with machine learning (ML) to differentiate prostate cancer cells with different metastatic abilities. The ML algorithms including principal component analysis (PCA) and nonnegative matrix factorization (NMF) were used for dimension reduction and feature detection. The characteristic component spectra were used to identify the key biomolecules that are correlated with metastatic potentials. The relative concentrations of the molecular spectral components were retrieved and used to classify the cancer cells with different metastatic potentials. A multi-class classification was performed using support vector machines (SVMs). The NFL spectral data were collected from three prostate cancer cell lines with different levels of metastatic potentials. The key biomolecules in the prostate cancer cells were identified to be tryptophan, reduced nicot...